Structure Prediction of Alternate Frame Folding Systems with AlphaFold3

AlphaFold has proven to be a valuable tool for predicting protein structures with unprecedented speed and accuracy. Extensive research from multiple groups has demonstrated that manipulating the multiple sequence alignment used for structure prediction can enhance AlphaFold2's ability to explore protein conformational landscapes, yielding reliable models of proteins capable of switching between alternative conformations. The release of the thoroughly reengineered AlphaFold3, which promises even greater prediction accuracy and efficiency, raises the question of whether such alternative conformational states can be modeled-either natively or by tuning the multiple sequence alignment used for prediction. In this work, we use a family of green fluorescent proteins engineered through alternate frame folding to assess AlphaFold3's prediction accuracy and uncover an unexpected role of disordered regions in driving the conformational preferences of the models.